import os, time, math, functools from pathlib import Path import multiprocessing from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, FUSE_CONV_BW from tinygrad.nn.state import get_parameters, get_state_dict, safe_load, safe_save from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup from extra.lr_scheduler import LRSchedulerGroup from examples.mlperf.helpers import get_training_state, load_training_state # TODO: fix benchmark logging and use tinygrad tqdm from tqdm import tqdm def train_resnet(): from extra.models import resnet from examples.mlperf.dataloader import batch_load_resnet from extra.datasets.imagenet import get_train_files, get_val_files from examples.mlperf.lr_schedulers import PolynomialDecayWithWarmup from examples.mlperf.initializers import Conv2dHeNormal, Linear from examples.hlb_cifar10 import UnsyncedBatchNorm config = {} seed = config["seed"] = getenv("SEED", 42) Tensor.manual_seed(seed) # seed for weight initialization INITMLPERF = getenv("INITMLPERF") RUNMLPERF = getenv("RUNMLPERF") if getenv("LOGMLPERF"): from mlperf_logging import mllog import mlperf_logging.mllog.constants as mllog_constants mllog.config(filename=f"result_resnet_{seed}.txt") mllog.config(root_dir=Path(__file__).parents[3].as_posix()) # truncate to log this. "file": "tinygrad/examples/mlperf/model_train.py" MLLOGGER = mllog.get_mllogger() if INITMLPERF: # common.yaml MLLOGGER.event(key=mllog_constants.SUBMISSION_ORG, value="tinycorp") MLLOGGER.event(key=mllog_constants.SUBMISSION_PLATFORM, value=getenv("SUBMISSION_PLATFORM", "tinybox")) MLLOGGER.event(key=mllog_constants.SUBMISSION_DIVISION, value=mllog_constants.CLOSED) MLLOGGER.event(key=mllog_constants.SUBMISSION_STATUS, value=mllog_constants.ONPREM) # closed_common.yaml MLLOGGER.event(key=mllog_constants.SUBMISSION_BENCHMARK, value=mllog_constants.RESNET) diskcache_clear() MLLOGGER.event(key=mllog_constants.CACHE_CLEAR, value=True) MLLOGGER.start(key=mllog_constants.INIT_START) if RUNMLPERF: MLLOGGER.start(key=mllog_constants.RUN_START) MLLOGGER.event(key=mllog_constants.SEED, value=seed) else: MLLOGGER = None GPUS = config["GPUS"] = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 1))] print(f"training on {GPUS}") for x in GPUS: Device[x] TRAIN_BEAM = getenv("TRAIN_BEAM", BEAM.value) EVAL_BEAM = getenv("EVAL_BEAM", BEAM.value) # ** model definition and initializers ** num_classes = 1000 resnet.Conv2d = Conv2dHeNormal resnet.Linear = Linear if not getenv("SYNCBN"): resnet.BatchNorm = functools.partial(UnsyncedBatchNorm, num_devices=len(GPUS)) model = resnet.ResNet50(num_classes) # shard weights and initialize in order for k, x in get_state_dict(model).items(): if not getenv("SYNCBN") and ("running_mean" in k or "running_var" in k): x.realize().shard_(GPUS, axis=0) else: x.realize().to_(GPUS) parameters = get_parameters(model) # ** hyperparameters ** epochs = config["epochs"] = getenv("EPOCHS", 37) BS = config["BS"] = getenv("BS", 104 * len(GPUS)) # fp32 GPUS<=6 7900xtx can fit BS=112 EVAL_BS = config["EVAL_BS"] = getenv("EVAL_BS", BS) base_lr = config["base_lr"] = getenv("LR", 7.2 * (BS/1536)) lr_warmup_epochs = config["lr_warmup_epochs"] = getenv("WARMUP_EPOCHS", 2) decay = config["decay"] = getenv("DECAY", 2e-4) loss_scaler = config["LOSS_SCALER"] = getenv("LOSS_SCALER", 256.0 if dtypes.default_float == dtypes.float16 else 1.0) target, achieved = getenv("TARGET", 0.759), False eval_start_epoch = getenv("EVAL_START_EPOCH", 0) eval_freq = getenv("EVAL_FREQ", 1) steps_in_train_epoch = config["steps_in_train_epoch"] = (round_up(len(get_train_files()), BS) // BS) steps_in_val_epoch = config["steps_in_val_epoch"] = (round_up(len(get_val_files()), EVAL_BS) // EVAL_BS) config["DEFAULT_FLOAT"] = dtypes.default_float.name config["BEAM"] = BEAM.value config["TRAIN_BEAM"] = TRAIN_BEAM config["EVAL_BEAM"] = EVAL_BEAM config["WINO"] = WINO.value config["SYNCBN"] = getenv("SYNCBN") # ** Optimizer ** skip_list = [v for k, v in get_state_dict(model).items() if "bn" in k or "bias" in k or "downsample.1" in k] parameters = [x for x in parameters if x not in set(skip_list)] optimizer = LARS(parameters, base_lr, momentum=.9, weight_decay=decay) optimizer_skip = SGD(skip_list, base_lr, momentum=.9, weight_decay=0.0, classic=True) optimizer_group = OptimizerGroup(optimizer, optimizer_skip) # ** LR scheduler ** scheduler = PolynomialDecayWithWarmup(optimizer, initial_lr=base_lr, end_lr=1e-4, train_steps=epochs * steps_in_train_epoch, warmup=lr_warmup_epochs * steps_in_train_epoch) scheduler_skip = PolynomialDecayWithWarmup(optimizer_skip, initial_lr=base_lr, end_lr=1e-4, train_steps=epochs * steps_in_train_epoch, warmup=lr_warmup_epochs * steps_in_train_epoch) scheduler_group = LRSchedulerGroup(scheduler, scheduler_skip) print(f"training with batch size {BS} for {epochs} epochs") # log mlperf hparams if MLLOGGER: if RUNMLPERF: MLLOGGER.event(key=mllog_constants.GLOBAL_BATCH_SIZE, value=BS) from extra.datasets.imagenet import get_train_files, get_val_files MLLOGGER.event(key=mllog_constants.TRAIN_SAMPLES, value=len(get_train_files())) MLLOGGER.event(key=mllog_constants.EVAL_SAMPLES, value=len(get_val_files())) MLLOGGER.event(key=mllog_constants.GRADIENT_ACCUMULATION_STEPS, value=1) MLLOGGER.event(key=mllog_constants.OPT_NAME, value="lars") assert scheduler.initial_lr == scheduler_skip.initial_lr assert scheduler.end_lr == scheduler_skip.end_lr assert scheduler.power == scheduler_skip.power MLLOGGER.event(key=mllog_constants.LARS_OPT_BASE_LEARNING_RATE, value=scheduler.initial_lr) MLLOGGER.event(key=mllog_constants.LARS_OPT_END_LR, value=scheduler.end_lr) MLLOGGER.event(key=mllog_constants.LARS_OPT_LR_DECAY_POLY_POWER, value=scheduler.power) MLLOGGER.event(key=mllog_constants.LARS_OPT_LR_DECAY_STEPS, value=epochs) MLLOGGER.event(key=mllog_constants.LARS_EPSILON, value=0) # does not support epsilon != 0 MLLOGGER.event(key=mllog_constants.LARS_OPT_LEARNING_RATE_WARMUP_EPOCHS, value=lr_warmup_epochs) MLLOGGER.event(key=mllog_constants.LARS_OPT_MOMENTUM, value=optimizer.momentum) MLLOGGER.event(key=mllog_constants.LARS_OPT_WEIGHT_DECAY, value=optimizer.wd) # ** resume from checkpointing ** start_epoch = 0 if ckpt:=getenv("RESUME", ""): load_training_state(model, optimizer_group, scheduler_group, safe_load(ckpt)) start_epoch = int(scheduler.epoch_counter.numpy().item() / steps_in_train_epoch) print(f"resuming from {ckpt} at epoch {start_epoch}") # ** init wandb ** WANDB = getenv("WANDB") if WANDB: import wandb wandb_args = {"id": wandb_id, "resume": "must"} if (wandb_id := getenv("WANDB_RESUME", "")) else {} wandb.init(config=config, **wandb_args) BENCHMARK = getenv("BENCHMARK") # ** jitted steps ** input_mean = Tensor([123.68, 116.78, 103.94], device=GPUS, dtype=dtypes.float32).reshape(1, -1, 1, 1) # mlperf reference resnet does not divide by input_std for some reason # input_std = Tensor([0.229, 0.224, 0.225], device=GPUS, dtype=dtypes.float32).reshape(1, -1, 1, 1) def normalize(x): return (x.permute([0, 3, 1, 2]) - input_mean).cast(dtypes.default_float) @TinyJit def train_step(X, Y): optimizer_group.zero_grad() X = normalize(X) out = model.forward(X) loss = out.cast(dtypes.float32).sparse_categorical_crossentropy(Y, label_smoothing=0.1) top_1 = (out.argmax(-1) == Y).sum() (loss * loss_scaler).backward() for t in optimizer_group.params: t.grad = t.grad.contiguous() / loss_scaler optimizer_group.step() scheduler_group.step() return loss.realize(), top_1.realize() @TinyJit def eval_step(X, Y): X = normalize(X) out = model.forward(X) loss = out.cast(dtypes.float32).sparse_categorical_crossentropy(Y, label_smoothing=0.1) top_1 = (out.argmax(-1) == Y).sum() return loss.realize(), top_1.realize() def fake_data_get(batch_size): x = Tensor.zeros(batch_size, 224, 224, 3, dtype=dtypes.uchar).contiguous() y = [0] * batch_size return x.shard(GPUS, axis=0).realize(), Tensor(y, requires_grad=False).shard(GPUS, axis=0), y, None def data_get(it): x, y, cookie = next(it) return x.shard(GPUS, axis=0).realize(), Tensor(y, requires_grad=False).shard(GPUS, axis=0), y, cookie # ** epoch loop ** step_times = [] for e in range(start_epoch, epochs): # ** train loop ** if MLLOGGER and RUNMLPERF: MLLOGGER.start(key=mllog_constants.EPOCH_START, value=e+1, metadata=dict(epoch_num=e+1)) Tensor.training = True BEAM.value = TRAIN_BEAM if INITMLPERF: i, proc = 0, fake_data_get(BS) else: batch_loader = batch_load_resnet(batch_size=BS, val=False, shuffle=True, seed=seed*epochs + e, pad_first_batch=True) it = iter(tqdm(batch_loader, total=steps_in_train_epoch, desc=f"epoch {e}", disable=BENCHMARK)) i, proc = 0, data_get(it) prev_cookies = [] st = time.perf_counter() while proc is not None: GlobalCounters.reset() (loss, top_1), y, proc = train_step(proc[0], proc[1]), proc[2], proc[3] pt = time.perf_counter() if len(prev_cookies) == getenv("STORE_COOKIES", 1): prev_cookies = [] # free previous cookies after gpu work has been enqueued try: if INITMLPERF: next_proc = fake_data_get(BS) else: next_proc = data_get(it) except StopIteration: next_proc = None dt = time.perf_counter() device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}" loss, top_1 = loss.numpy().item(), top_1.numpy().item() top_1_acc = top_1 / sum(yi != -1 for yi in y) cl = time.perf_counter() if BENCHMARK: step_times.append(cl - st) tqdm.write( f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, " f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {top_1_acc:3.2f} acc, {optimizer.lr.numpy()[0]:.6f} LR, " f"{GlobalCounters.mem_used / 1e9:.2f} GB used, {GlobalCounters.global_ops * 1e-9 / (cl - st):9.2f} GFLOPS") if WANDB: wandb.log({"lr": optimizer.lr.numpy(), "train/loss": loss, "train/top_1_acc": top_1_acc, "train/step_time": cl - st, "train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt, "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": e + (i + 1) / steps_in_train_epoch}) st = cl prev_cookies.append(proc) proc, next_proc = next_proc, None # return old cookie i += 1 if i == BENCHMARK: assert not math.isnan(loss) median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2] # in seconds estimated_total_minutes = int(median_step_time * steps_in_train_epoch * epochs / 60) print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m") print(f"epoch global_ops: {steps_in_train_epoch * GlobalCounters.global_ops:_}, " f"epoch global_mem: {steps_in_train_epoch * GlobalCounters.global_mem:_}") # if we are doing beam search, run the first eval too if (TRAIN_BEAM or EVAL_BEAM) and e == start_epoch: break return if MLLOGGER and RUNMLPERF: MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=e+1, metadata=dict(epoch_num=e+1)) # ** eval loop ** # always eval for epoch >= 33 to stop the clock as soon as eval target hits, it can converge in epoch in [33, 37] if steps_in_val_epoch > 0 and ((e + 1 - eval_start_epoch) % eval_freq == 0 or e + 1 >= 33): if MLLOGGER and RUNMLPERF: MLLOGGER.start(key=mllog_constants.EVAL_START, value=e+1, metadata=dict(epoch_num=e+1)) if getenv("RESET_STEP", 1): train_step.reset() # free the train step memory :( eval_times = [] eval_loss = 0.0 eval_top_1 = 0 eval_num_samples = 0 Tensor.training = False BEAM.value = EVAL_BEAM if INITMLPERF: i, proc = 0, fake_data_get(EVAL_BS) else: it = iter(tqdm(batch_load_resnet(batch_size=EVAL_BS, val=True, shuffle=False, pad_first_batch=True), total=steps_in_val_epoch)) i, proc = 0, data_get(it) prev_cookies = [] while proc is not None: GlobalCounters.reset() st = time.time() (loss, top_1), y, proc = eval_step(proc[0], proc[1]), proc[2], proc[3] # drop inputs, keep cookie if len(prev_cookies) == getenv("STORE_COOKIES", 1): prev_cookies = [] # free previous cookies after gpu work has been enqueued try: if INITMLPERF: next_proc = fake_data_get(EVAL_BS) else: next_proc = data_get(it) except StopIteration: next_proc = None loss, top_1 = loss.numpy().item(), top_1.numpy().item() num_samples = sum(yi != -1 for yi in y) eval_loss += loss * num_samples eval_top_1 += top_1 eval_num_samples += num_samples prev_cookies.append(proc) proc, next_proc = next_proc, None i += 1 if i == BENCHMARK: # assume INITMLPERF has BENCHMARK set if MLLOGGER and INITMLPERF: MLLOGGER.event(key=mllog_constants.INIT_STOP) return et = time.time() eval_times.append(et - st) if getenv("RESET_STEP", 1): eval_step.reset() if not BENCHMARK: assert eval_num_samples == len(get_val_files()), f"eval sample count mismatched. {eval_num_samples=} != {len(get_val_files())}" total_loss = eval_loss / eval_num_samples total_top_1 = eval_top_1 / eval_num_samples total_fw_time = sum(eval_times) / len(eval_times) tqdm.write(f"eval loss: {total_loss:.2f}, eval time: {total_fw_time:.2f}, eval top 1 acc: {total_top_1:.3f}") if WANDB: wandb.log({"eval/loss": total_loss, "eval/top_1_acc": total_top_1, "eval/forward_time": total_fw_time, "epoch": e + 1}) if MLLOGGER and RUNMLPERF: MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=total_top_1, metadata=dict(epoch_num=e+1)) MLLOGGER.event(key=mllog_constants.EVAL_STOP, value=e+1, metadata=dict(epoch_num=e+1)) # save model if achieved target if not achieved and total_top_1 >= target: # stop once achieve the target if MLLOGGER and RUNMLPERF: MLLOGGER.event(key=mllog_constants.RUN_STOP, metadata=dict(status=mllog_constants.SUCCESS)) if not os.path.exists("./ckpts"): os.mkdir("./ckpts") fn = f"./ckpts/resnet50_{seed}.safe" safe_save(get_state_dict(model), fn) print(f" *** Model saved to {fn} ***") achieved = True break # checkpoint every time we eval if getenv("CKPT"): if not os.path.exists("./ckpts"): os.mkdir("./ckpts") if WANDB and wandb.run is not None: fn = f"./ckpts/{time.strftime('%Y%m%d_%H%M%S')}_{wandb.run.id}_e{e}.safe" else: fn = f"./ckpts/{time.strftime('%Y%m%d_%H%M%S')}_e{e}.safe" print(f"saving ckpt to {fn}") safe_save(get_training_state(model, optimizer_group, scheduler_group), fn) def train_retinanet(): # TODO: Retinanet pass def train_unet3d(): """ Trains the UNet3D model. Instructions: 1) Run the following script from the root folder of `tinygrad`: ```./examples/mlperf/scripts/setup_kits19_dataset.sh``` Optionally, `BASEDIR` can be set to download and process the dataset at a specific location: ```BASEDIR= ./examples/mlperf/scripts/setup_kits19_dataset.sh``` 2) To start training the model, run the following: ```time PYTHONPATH=. WANDB=1 TRAIN_BEAM=3 FUSE_CONV_BW=1 GPUS=6 BS=6 MODEL=unet3d python3 examples/mlperf/model_train.py``` """ from examples.mlperf.losses import dice_ce_loss from examples.mlperf.metrics import dice_score from examples.mlperf.dataloader import batch_load_unet3d from extra.models.unet3d import UNet3D from extra.datasets.kits19 import iterate, get_train_files, get_val_files, sliding_window_inference, preprocess_dataset, TRAIN_PREPROCESSED_DIR, VAL_PREPROCESSED_DIR from tinygrad import Context from tinygrad.nn.optim import SGD from math import ceil GPUS = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 1))] for x in GPUS: Device[x] TARGET_METRIC = 0.908 NUM_EPOCHS = getenv("NUM_EPOCHS", 4000) BS = getenv("BS", 1 * len(GPUS)) LR = getenv("LR", 2.0 * (BS / 28)) LR_WARMUP_EPOCHS = getenv("LR_WARMUP_EPOCHS", 1000) LR_WARMUP_INIT_LR = getenv("LR_WARMUP_INIT_LR", 0.0001) WANDB = getenv("WANDB") PROJ_NAME = getenv("PROJ_NAME", "tinygrad_unet3d_mlperf") SEED = getenv("SEED", -1) if getenv("SEED", -1) >= 0 else None TRAIN_DATASET_SIZE, VAL_DATASET_SIZE = len(get_train_files()), len(get_val_files()) SAMPLES_PER_EPOCH = TRAIN_DATASET_SIZE // BS START_EVAL_AT = getenv("START_EVAL_AT", ceil(1000 * TRAIN_DATASET_SIZE / (SAMPLES_PER_EPOCH * BS))) EVALUATE_EVERY = getenv("EVALUATE_EVERY", ceil(20 * TRAIN_DATASET_SIZE / (SAMPLES_PER_EPOCH * BS))) TRAIN_BEAM, EVAL_BEAM = getenv("TRAIN_BEAM", BEAM.value), getenv("EVAL_BEAM", BEAM.value) BENCHMARK = getenv("BENCHMARK") CKPT = getenv("CKPT") config = { "num_epochs": NUM_EPOCHS, "batch_size": BS, "learning_rate": LR, "learning_rate_warmup_epochs": LR_WARMUP_EPOCHS, "learning_rate_warmup_init": LR_WARMUP_INIT_LR, "start_eval_at": START_EVAL_AT, "evaluate_every": EVALUATE_EVERY, "train_beam": TRAIN_BEAM, "eval_beam": EVAL_BEAM, "wino": WINO.value, "fuse_conv_bw": FUSE_CONV_BW.value, "gpus": GPUS, "default_float": dtypes.default_float.name } if WANDB: try: import wandb except ImportError: raise "Need to install wandb to use it" if SEED is not None: config["seed"] = SEED Tensor.manual_seed(SEED) model = UNet3D() params = get_parameters(model) for p in params: p.realize().to_(GPUS) optim = SGD(params, lr=LR, momentum=0.9, nesterov=True) def lr_warm_up(optim, init_lr, lr, current_epoch, warmup_epochs): scale = current_epoch / warmup_epochs optim.lr.assign(Tensor([init_lr + (lr - init_lr) * scale], device=GPUS)).realize() def save_checkpoint(state_dict, fn): if not os.path.exists("./ckpts"): os.mkdir("./ckpts") print(f"saving checkpoint to {fn}") safe_save(state_dict, fn) def data_get(it): x, y, cookie = next(it) return x.shard(GPUS, axis=0).realize(), y.shard(GPUS, axis=0), cookie @TinyJit @Tensor.train() def train_step(model, x, y): optim.zero_grad() y_hat = model(x) loss = dice_ce_loss(y_hat, y) loss.backward() optim.step() return loss.realize() @Tensor.train(mode=False) @Tensor.test() def eval_step(model, x, y): y_hat, y = sliding_window_inference(model, x, y, gpus=GPUS) y_hat, y = Tensor(y_hat), Tensor(y, requires_grad=False) loss = dice_ce_loss(y_hat, y) score = dice_score(y_hat, y) return loss.realize(), score.realize() if WANDB: wandb.init(config=config, project=PROJ_NAME) step_times, start_epoch = [], 1 is_successful, diverged = False, False start_eval_at, evaluate_every = 1 if BENCHMARK else START_EVAL_AT, 1 if BENCHMARK else EVALUATE_EVERY next_eval_at = start_eval_at print(f"Training on {GPUS}") if BENCHMARK: print("Benchmarking UNet3D") else: print(f"Start evaluation at epoch {start_eval_at} and every {evaluate_every} epoch(s) afterwards") if not TRAIN_PREPROCESSED_DIR.exists(): preprocess_dataset(get_train_files(), TRAIN_PREPROCESSED_DIR, False) if not VAL_PREPROCESSED_DIR.exists(): preprocess_dataset(get_val_files(), VAL_PREPROCESSED_DIR, True) for epoch in range(1, NUM_EPOCHS + 1): with Context(BEAM=TRAIN_BEAM): if epoch <= LR_WARMUP_EPOCHS and LR_WARMUP_EPOCHS > 0: lr_warm_up(optim, LR_WARMUP_INIT_LR, LR, epoch, LR_WARMUP_EPOCHS) train_dataloader = batch_load_unet3d(TRAIN_PREPROCESSED_DIR, batch_size=BS, val=False, shuffle=True, seed=SEED) it = iter(tqdm(train_dataloader, total=SAMPLES_PER_EPOCH, desc=f"epoch {epoch}", disable=BENCHMARK)) i, proc = 0, data_get(it) prev_cookies = [] st = time.perf_counter() while proc is not None: GlobalCounters.reset() loss, proc = train_step(model, proc[0], proc[1]), proc[2] pt = time.perf_counter() if len(prev_cookies) == getenv("STORE_COOKIES", 1): prev_cookies = [] # free previous cookies after gpu work has been enqueued try: next_proc = data_get(it) except StopIteration: next_proc = None dt = time.perf_counter() device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}" loss = loss.numpy().item() cl = time.perf_counter() if BENCHMARK: step_times.append(cl - st) tqdm.write( f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, " f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {optim.lr.numpy()[0]:.6f} LR, " f"{GlobalCounters.mem_used / 1e9:.2f} GB used, {GlobalCounters.global_ops * 1e-9 / (cl - st):9.2f} GFLOPS" ) if WANDB: wandb.log({"lr": optim.lr.numpy(), "train/loss": loss, "train/step_time": cl - st, "train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt, "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": epoch + (i + 1) / SAMPLES_PER_EPOCH}) st = cl prev_cookies.append(proc) proc, next_proc = next_proc, None # return old cookie i += 1 if i == BENCHMARK: median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2] # in seconds estimated_total_minutes = int(median_step_time * SAMPLES_PER_EPOCH * NUM_EPOCHS / 60) print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m") if (TRAIN_BEAM or EVAL_BEAM) and epoch == start_epoch: break return with Context(BEAM=EVAL_BEAM): if epoch == next_eval_at: next_eval_at += evaluate_every eval_loss = [] scores = [] for x, y in tqdm(iterate(get_val_files(), preprocessed_dir=VAL_PREPROCESSED_DIR), total=VAL_DATASET_SIZE): eval_loss_value, score = eval_step(model, x, y) eval_loss.append(eval_loss_value) scores.append(score) scores = Tensor.mean(Tensor.stack(*scores, dim=0), axis=0).numpy() eval_loss = Tensor.mean(Tensor.stack(*eval_loss, dim=0), axis=0).numpy() l1_dice, l2_dice = scores[0][-2], scores[0][-1] mean_dice = (l2_dice + l1_dice) / 2 tqdm.write(f"{l1_dice} L1 dice, {l2_dice} L2 dice, {mean_dice:.3f} mean_dice, {eval_loss:5.2f} eval_loss") if WANDB: wandb.log({"eval/loss": eval_loss, "eval/mean_dice": mean_dice, "epoch": epoch}) if mean_dice >= TARGET_METRIC: is_successful = True save_checkpoint(get_state_dict(model), f"./ckpts/unet3d.safe") elif mean_dice < 1e-6: print("Model diverging. Aborting.") diverged = True if not is_successful and CKPT: if WANDB and wandb.run is not None: fn = f"./ckpts/{time.strftime('%Y%m%d_%H%M%S')}_{wandb.run.id}_e{epoch}.safe" else: fn = f"./ckpts/{time.strftime('%Y%m%d_%H%M%S')}_e{epoch}.safe" save_checkpoint(get_state_dict(model), fn) if is_successful or diverged: break def train_rnnt(): # TODO: RNN-T pass @TinyJit def train_step_bert(model, optimizer, scheduler, loss_scaler:float, input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor, masked_positions:Tensor, masked_lm_ids:Tensor, masked_lm_weights:Tensor, next_sentence_labels:Tensor, GPUS): for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]: if len(GPUS) > 1: t.shard_(GPUS, axis=0) else: t.to_(GPUS[0]) optimizer.zero_grad() lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids) loss = model.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels) (loss * loss_scaler).backward() global_norm = Tensor([0.0], dtype=dtypes.float32, device=optimizer[0].device).realize() for p in optimizer.params: p.grad = p.grad / loss_scaler global_norm += p.grad.float().square().sum() global_norm = global_norm.sqrt() for p in optimizer.params: p.grad = (p.grad / Tensor.where(global_norm > 1.0, global_norm, 1.0)).cast(p.grad.dtype) optimizer.step() scheduler.step() # TODO: no to("CPU") here because it blocks and messes the python time Tensor.realize(loss, global_norm, optimizer.optimizers[0].lr) return loss, global_norm, optimizer.optimizers[0].lr @TinyJit def eval_step_bert(model, input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor, masked_positions:Tensor, masked_lm_ids:Tensor, masked_lm_weights:Tensor, next_sentence_labels:Tensor, GPUS): for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]: if len(GPUS) > 1: t.shard_(GPUS, axis=0) else: t.to_(GPUS[0]) lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids) masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss = \ model.accuracy(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels) for t in [masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss]: t.to_("CPU") Tensor.realize(masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss) return masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss def train_bert(): # NOTE: pip install tensorflow, wandb required from examples.mlperf.dataloader import batch_load_train_bert, batch_load_val_bert from examples.mlperf.helpers import get_mlperf_bert_model, get_fake_data_bert from examples.mlperf.lr_schedulers import PolynomialDecayWithWarmup config = {} BASEDIR = getenv("BASEDIR", Path(__file__).parent.parents[1] / "extra" / "datasets" / "wiki") GPUS = config["GPUS"] = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 1))] print(f"training on {GPUS}") for x in GPUS: Device[x] seed = config["seed"] = getenv("SEED", 12345) INITMLPERF = getenv("INITMLPERF") RUNMLPERF = getenv("RUNMLPERF") BENCHMARK = getenv("BENCHMARK") if getenv("LOGMLPERF"): from mlperf_logging import mllog import mlperf_logging.mllog.constants as mllog_constants mllog.config(filename=f"result_bert_{seed}.log") mllog.config(root_dir=Path(__file__).parents[3].as_posix()) MLLOGGER = mllog.get_mllogger() MLLOGGER.logger.propagate = False if INITMLPERF: assert BENCHMARK, f"BENCHMARK must be set for INITMLPERF" MLLOGGER.event(key=mllog_constants.SUBMISSION_ORG, value="tinycorp") MLLOGGER.event(key=mllog_constants.SUBMISSION_PLATFORM, value=getenv("SUBMISSION_PLATFORM", "tinybox")) MLLOGGER.event(key=mllog_constants.SUBMISSION_DIVISION, value=mllog_constants.CLOSED) MLLOGGER.event(key=mllog_constants.SUBMISSION_STATUS, value=mllog_constants.ONPREM) MLLOGGER.event(key=mllog_constants.SUBMISSION_BENCHMARK, value=mllog_constants.BERT) diskcache_clear() MLLOGGER.event(key=mllog_constants.CACHE_CLEAR, value=True) MLLOGGER.start(key=mllog_constants.INIT_START, value=None) if RUNMLPERF: MLLOGGER.start(key=mllog_constants.RUN_START, value=None) MLLOGGER.event(key=mllog_constants.SEED, value=seed) else: MLLOGGER = None # ** hyperparameters ** BS = config["GLOBAL_BATCH_SIZE"] = getenv("BS", 11 * len(GPUS) if dtypes.default_float in (dtypes.float16, dtypes.bfloat16) else 8 * len(GPUS)) EVAL_BS = config["EVAL_BS"] = getenv("EVAL_BS", 1 * len(GPUS)) max_lr = config["OPT_BASE_LEARNING_RATE"] = getenv("OPT_BASE_LEARNING_RATE", 0.000175 * math.sqrt(BS/96)) train_steps = config["TRAIN_STEPS"] = getenv("TRAIN_STEPS", 3300000 // BS) warmup_steps = config["NUM_WARMUP_STEPS"] = getenv("NUM_WARMUP_STEPS", 1) max_eval_steps = config["MAX_EVAL_STEPS"] = getenv("MAX_EVAL_STEPS", (10000 + EVAL_BS - 1) // EVAL_BS) # EVAL_BS * MAX_EVAL_STEPS >= 10000 eval_step_freq = config["EVAL_STEP_FREQ"] = getenv("EVAL_STEP_FREQ", int((math.floor(0.05 * (230.23 * BS + 3000000) / 25000) * 25000) / BS)) # Round down save_ckpt_freq = config["SAVE_CKPT_FREQ"] = getenv("SAVE_CKPT_FREQ", 1000) keep_ckpt_amount = config["KEEP_CKPT_AMOUNT"] = getenv("KEEP_CKPT_AMOUNT", 5) save_ckpt_dir = config["SAVE_CKPT_DIR"] = getenv("SAVE_CKPT_DIR", "./ckpts") init_ckpt = config["INIT_CKPT_DIR"] = getenv("INIT_CKPT_DIR", BASEDIR) loss_scaler = config["LOSS_SCALER"] = getenv("LOSS_SCALER", 2.0**11 if dtypes.default_float == dtypes.float16 else 1.0) decay = config["DECAY"] = getenv("DECAY", 0.01) epsilon = config["EPSILON"] = getenv("EPSILON", 1e-6) poly_power = config["POLY_POWER"] = getenv("POLY_POWER", 1.0) target, achieved = getenv("TARGET", 0.72), False config["DEFAULT_FLOAT"] = dtypes.default_float.name config["DISABLE_DROPOUT"] = getenv("DISABLE_DROPOUT", 0) config["TRAIN_BEAM"] = TRAIN_BEAM = getenv("TRAIN_BEAM", BEAM.value) config["EVAL_BEAM"] = EVAL_BEAM = getenv("EVAL_BEAM", BEAM.value) Tensor.manual_seed(seed) # seed for weight initialization assert 10000 <= (EVAL_BS * max_eval_steps), "Evaluation batchsize * max_eval_steps must greater or equal 10000 to iterate over full eval dataset" # ** init wandb ** WANDB = getenv("WANDB") if WANDB: import wandb wandb_args = {"id": wandb_id, "resume": "must"} if (wandb_id := getenv("WANDB_RESUME", "")) else {} wandb.init(config=config, **wandb_args, project="MLPerf-BERT") # ** init model ** model = get_mlperf_bert_model() if RUNMLPERF: model.load_from_pretrained(init_ckpt) else: # for init, zero out all weights for p in get_parameters(model): p = p.assign(Tensor.zeros_like(p).contiguous()).realize() parameters = get_parameters(model) if len(GPUS) > 1: for p in parameters: p.to_(GPUS) # ** Log run config ** for key, value in config.items(): print(f'HParam: "{key}": {value}') # ** Optimizer ** parameters_no_wd = [v for k, v in get_state_dict(model).items() if "bias" in k or "LayerNorm" in k] parameters = [x for x in parameters if x not in set(parameters_no_wd)] optimizer_wd = LAMB(parameters, lr=max_lr, eps=epsilon, weight_decay=decay, adam=False) optimizer_no_wd = LAMB(parameters_no_wd, lr=max_lr, eps=epsilon, weight_decay=0.0, adam=False) optimizer_group = OptimizerGroup(optimizer_wd, optimizer_no_wd) # ** LR scheduler ** scheduler_wd = PolynomialDecayWithWarmup(optimizer_wd, max_lr, 0, train_steps, warmup_steps, power=poly_power) scheduler_no_wd = PolynomialDecayWithWarmup(optimizer_no_wd, max_lr, 0, train_steps, warmup_steps, power=poly_power) scheduler_group = LRSchedulerGroup(scheduler_wd, scheduler_no_wd) print(f"training with batch size {BS} for one epoch with {train_steps} steps") # log mlperf hparams if MLLOGGER: if RUNMLPERF: MLLOGGER.event(key=mllog_constants.GLOBAL_BATCH_SIZE, value=config["GLOBAL_BATCH_SIZE"]) MLLOGGER.event(key=mllog_constants.MAX_SEQUENCE_LENGTH, value=512) MLLOGGER.event(key="max_predictions_per_seq", value=76) MLLOGGER.event(key=mllog_constants.OPT_NAME, value="LAMB") MLLOGGER.event(key=mllog_constants.OPT_BASE_LR, value=config["OPT_BASE_LEARNING_RATE"]) MLLOGGER.event(key=mllog_constants.OPT_LAMB_WEIGHT_DECAY, value=config["DECAY"]) MLLOGGER.event(key=mllog_constants.OPT_LAMB_BETA_1, value=optimizer_wd.b1) MLLOGGER.event(key=mllog_constants.OPT_LAMB_BETA_2, value=optimizer_wd.b2) MLLOGGER.event(key=mllog_constants.OPT_LAMB_LR_DECAY_POLY_POWER, value=config["POLY_POWER"]) MLLOGGER.event(key=mllog_constants.OPT_LAMB_EPSILON, value=config["EPSILON"]) MLLOGGER.event(key=mllog_constants.OPT_LR_WARMUP_STEPS, value=config["NUM_WARMUP_STEPS"]) MLLOGGER.event(key=mllog_constants.NUM_WARMUP_STEPS, value=config["NUM_WARMUP_STEPS"]) MLLOGGER.event(key='start_warmup_step', value=0) MLLOGGER.event(key='opt_learning_rate_training_steps', value=config["TRAIN_STEPS"]) MLLOGGER.event(key=mllog_constants.GRADIENT_ACCUMULATION_STEPS, value=1) MLLOGGER.event(key=mllog_constants.EVAL_SAMPLES, value=config["EVAL_BS"] * config["MAX_EVAL_STEPS"]) MLLOGGER.event(key=mllog_constants.TRAIN_SAMPLES, value=config["GLOBAL_BATCH_SIZE"] * config["TRAIN_STEPS"]) # ** resume from checkpointing ** start_step = 0 previous_step = None if ckpt:=getenv("RESUME", ""): load_training_state(model, optimizer_group, scheduler_group, safe_load(ckpt)) start_step = int(scheduler_wd.epoch_counter.item()) print(f"resuming from {ckpt} at step {start_step}") if RUNMLPERF: # only load real data with RUNMLPERF eval_it = iter(batch_load_val_bert(EVAL_BS)) train_it = iter(tqdm(batch_load_train_bert(BS), total=train_steps, disable=BENCHMARK)) for _ in range(start_step): next(train_it) # Fast forward else: # repeat fake data def repeat_fake(bs): while True: yield get_fake_data_bert(bs) eval_it = iter(repeat_fake(EVAL_BS)) train_it = iter(repeat_fake(BS)) step_times = [] # ** train loop ** wc_start = time.perf_counter() i, train_data = start_step, next(train_it) if RUNMLPERF: if MLLOGGER: MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i*BS, metadata={"epoch_num": i*BS}) while train_data is not None and i < train_steps and not achieved: if getenv("TRAIN", 1): Tensor.training = True BEAM.value = TRAIN_BEAM st = time.perf_counter() GlobalCounters.reset() loss, global_norm, lr = train_step_bert(model, optimizer_group, scheduler_group, loss_scaler, train_data["input_ids"], train_data["segment_ids"], train_data["input_mask"], train_data["masked_lm_positions"], \ train_data["masked_lm_ids"], train_data["masked_lm_weights"], train_data["next_sentence_labels"], GPUS) pt = time.perf_counter() try: next_data = next(train_it) except StopIteration: next_data = None dt = time.perf_counter() device_str = parameters[0].device if isinstance(parameters[0].device, str) else f"{parameters[0].device[0]} * {len(parameters[0].device)}" loss = loss.item() lr = lr.item() cl = time.perf_counter() if BENCHMARK: step_times.append(cl - st) tqdm.write( f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, " f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {lr:.6f} LR, " f"{GlobalCounters.mem_used / 1e9:.2f} GB used, {GlobalCounters.global_ops * 1e-9 / (cl - st):9.2f} GFLOPS") if WANDB: wandb.log({"lr": lr, "train/loss": loss, "train/global_norm": global_norm.item(), "train/step_time": cl - st, "train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt, "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*BS}) train_data, next_data = next_data, None i += 1 if i == BENCHMARK: median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2] # in seconds estimated_total_minutes = int(median_step_time * train_steps / 60) print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m") print(f"epoch global_ops: {train_steps * GlobalCounters.global_ops:_}, " f"epoch global_mem: {train_steps * GlobalCounters.global_mem:_}") # ** eval loop ** if i % eval_step_freq == 0 or (BENCHMARK and i == BENCHMARK) or i == train_steps: if MLLOGGER and RUNMLPERF: MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*BS, "step_num": i}) if getenv("RESET_STEP", 0): train_step_bert.reset() elif train_step_bert.captured is not None: train_step_bert.captured.free_intermediates() eval_lm_losses = [] eval_clsf_losses = [] eval_lm_accs = [] eval_clsf_accs = [] eval_times = [] Tensor.training = False BEAM.value = EVAL_BEAM for j in tqdm(range(max_eval_steps), desc="Evaluating", total=max_eval_steps, disable=BENCHMARK): eval_data = next(eval_it) GlobalCounters.reset() st = time.time() lm_acc, clsf_acc, lm_loss, clsf_loss = eval_step_bert(model, eval_data["input_ids"], eval_data["segment_ids"], eval_data["input_mask"], eval_data["masked_lm_positions"], eval_data["masked_lm_ids"], eval_data["masked_lm_weights"], eval_data["next_sentence_labels"], GPUS) lm_acc, clsf_acc, lm_loss, clsf_loss = lm_acc.item(), clsf_acc.item(), lm_loss.item(), clsf_loss.item() eval_lm_losses.append(lm_loss) eval_clsf_losses.append(clsf_loss) eval_lm_accs.append(lm_acc) eval_clsf_accs.append(clsf_acc) et = time.time() eval_times.append(et - st) if BENCHMARK and (j+1) == min(BENCHMARK, max_eval_steps): # assume INITMLPERF has BENCHMARK set if MLLOGGER and INITMLPERF: MLLOGGER.event(key=mllog_constants.INIT_STOP, value=None) return if getenv("RESET_STEP", 0): eval_step_bert.reset() elif eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates() del eval_data avg_lm_loss = sum(eval_lm_losses) / len(eval_lm_losses) avg_clsf_loss = sum(eval_clsf_losses) / len(eval_clsf_losses) avg_lm_acc = sum(eval_lm_accs) / len(eval_lm_accs) avg_clsf_acc = sum(eval_clsf_accs) / len(eval_clsf_accs) avg_fw_time = sum(eval_times) / len(eval_times) results = f"eval lm loss: {avg_lm_loss:.2f}, eval clsf loss: {avg_clsf_loss:.2f}, eval lm accuracy: {avg_lm_acc:.6f}, \ eval clsf accuracy: {avg_clsf_acc:.2f}, avg eval step time: {avg_fw_time:.2f}" tqdm.write(results) if WANDB: wandb.log({"eval/lm_loss": avg_lm_loss, "eval/clsf_loss": avg_clsf_loss, "eval/lm_accuracy": avg_lm_acc, \ "eval/clsf_accuracy": avg_clsf_acc, "eval/forward_time": avg_fw_time}) if MLLOGGER and RUNMLPERF: MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=i*BS, metadata={"epoch_count": i*BS, "step_num": i, "samples_count": config["EVAL_BS"] * config["MAX_EVAL_STEPS"]}) MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=avg_lm_acc, metadata={"epoch_num": i*BS, "masked_lm_accuracy": avg_lm_acc}) # save model if achieved target if not achieved and avg_lm_acc >= target: wc_end = time.perf_counter() if getenv("CKPT"): if not os.path.exists(ckpt_dir := save_ckpt_dir): os.mkdir(ckpt_dir) fn = f"{ckpt_dir}/bert-large.safe" safe_save(get_state_dict(model), fn) print(f" *** Model saved to {fn} ***") total_seconds = wc_end - wc_start hours = int(total_seconds // 3600) minutes = int((total_seconds % 3600) // 60) seconds = total_seconds % 60 print(f"Reference Convergence point reached after {i * BS} datasamples and {hours}h{minutes}m{seconds:.2f}s.") achieved = True if MLLOGGER and RUNMLPERF: MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=i*BS, metadata={"epoch_num": i*BS}) MLLOGGER.end(key=mllog_constants.RUN_STOP, metadata=dict(status=mllog_constants.SUCCESS)) # stop once hitting the target break # should not happen, BENCHMARK not properly terminated if BENCHMARK: assert i < BENCHMARK, i if getenv("CKPT") and i % save_ckpt_freq == 0: if MLLOGGER and RUNMLPERF: if previous_step: MLLOGGER.end(key=mllog_constants.BLOCK_STOP, value=None, metadata={"first_epoch_num": 1, "epoch_num": 1, "first_step_num": i, "step_num": i, "step_count": i - previous_step}) MLLOGGER.start(key="checkpoint_start", value=None, metadata={"step_num" : i}) if not os.path.exists(ckpt_dir := save_ckpt_dir): os.mkdir(ckpt_dir) if WANDB and wandb.run is not None: fn = f"{ckpt_dir}/{time.strftime('%Y%m%d_%H%M%S')}_{wandb.run.id}.safe" else: fn = f"{ckpt_dir}/{time.strftime('%Y%m%d_%H%M%S')}.safe" print(f"saving ckpt to {fn}") safe_save(get_training_state(model, optimizer_group, scheduler_group), fn) ckpt_files = [f for f in os.listdir(ckpt_dir) if os.path.isfile(os.path.join(ckpt_dir, f))] ckpt_files.sort(key=lambda x: os.path.getmtime(os.path.join(ckpt_dir, x))) while len(ckpt_files) > keep_ckpt_amount: last = ckpt_files.pop(0) print(f"Removing old ckpt {last}") os.remove(os.path.join(ckpt_dir, last)) if MLLOGGER and RUNMLPERF: MLLOGGER.end(key="checkpoint_stop", value=None, metadata={"step_num": i}) MLLOGGER.start(key=mllog_constants.BLOCK_START, value=None, metadata={"first_epoch_num": 1, "epoch_num": 1, "epoch_count": 1, "samples_count": i * BS, "step_num": i, "first_step_num": i+1}) previous_step = i def train_maskrcnn(): # TODO: Mask RCNN pass if __name__ == "__main__": multiprocessing.set_start_method('spawn') with Tensor.train(): for m in getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,maskrcnn").split(","): nm = f"train_{m}" if nm in globals(): print(f"training {m}") globals()[nm]()